Staff ML Systems Engineer — TPU Performance & Gemini

AI Chopping Block, Inc.; Sunnyvale (CA); On-site; USD 207,000 - 300,000; Full time

Job summary

Google is seeking software engineers for the Core ML team to advance ML workloads on TPU systems, including JAX and PyTorch, impacting Gemini and OSS models. You will contribute to performance optimization across large-scale ML projects and collaborate with researchers to push hardware and software boundaries.

Ideal candidates have extensive ML deployment experience, strong systems design skills, and a track record of delivering scalable, efficient software for massive-scale AI workloads.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience with ML design, ML infrastructure, or related fields.
• 5 years of experience testing, launching software products, and 3 years of software design/architecture.

Responsibilities

• Identify and maintain ML training and serving benchmarks representative to Google production and broader ML industry.
• Achieve performance for customer launches and OSS models, benchmarks submissions ML commons, InferenceMAX, etc.
• Use benchmarks to identify performance opportunities and drive improvements in compiler/runtime across teams.
• Collaborate with Google Product teams and researchers to solve performance problems and onboard new ML models on TPU hardware.
• Analyze performance and efficiency metrics to identify bottlenecks and scale improvements fleet-wide.

Skills

Software development

ML design

ML infrastructure

Model deployment

Debugging

Education

Bachelor’s degree or equivalent practical experience

Tools

Tensor Processing Units (TPU)

JAX

PyTorch

Job description

Google is seeking software engineers for the Core ML team to advance ML workloads on TPU systems, including JAX and PyTorch, impacting Gemini and OSS models. You will contribute to performance optimization across large-scale ML projects and collaborate with researchers to push hardware and software boundaries.

Ideal candidates have extensive ML deployment experience, strong systems design skills, and a track record of delivering scalable, efficient software for massive-scale AI workloads.